Internship Fast Physics

About us

Damen aims to become the worlds most sustainable and digitally connected shipyard. The Research Development & Innovation(RD&I)department develops and implements the technology andknow-howto achieve these ambitions. We activelyassistthe business in creating an innovative product portfolio andprovideforward-thinking guidance to improve the quality and performance of Damens products and services.You will be joining theData Science teamwithin Damen RD&IlocatedinGorinchem. Our department focuses on applyingcutting-edgedata and AI solutions to Damens shipbuilding and maritime operations.The team includes domain experts inphysics-informed machine learning simulation acceleration predictive maintenance computer vision and operational analytics. This internship is part of a strategic project aimed at accelerating complex simulations for ship performance usingmachine learning and graph-based AI.

Therole

As an intern you will work on theFast Physics project which aims to drastically reduce the runtime of high-fidelitycomputational fluid dynamics (CFD)simulationsofshiphulls. These simulations are essential in predicting how a vessel behaves in water but they can take hoursto compute. Instead of running time-consuming physics-based simulations weusegeometricdeep learninga type of machine learning that can learn fromvesseldesignsandquickly estimate results like water resistance or flow around the hull.The outcome is a working prototype that can support early-stage design exploration and simulation optimization.

You will contribute to enhancing the performance of an existing system that predicts physical quantitiessuch as ship resistance and flow fieldsbased on geometry and operating conditions. Your primary focus will be on a dedicated topic involving thetraining validationandextensionof the framework to supportmultiple ship typesand/orvarying levels of simulation fidelity.The assignment can bea thesis/graduateinternshipandcouldstart fromSeptemberonwards.

Keyaccountabilities

• Support theimprovementofML-basedframeworks focusing ongeometric deep learning andgraph neural networks.

• Preprocess CFD simulation data and ship hullgeometries.

• Run experiments inPython usingPyTorch.

• Workcloselyinourteam together with Data Scientists domainknowledgenaval architectsand external partners such as MARIN.

• Document results and present findings to the team regularly.

Skills & Experience

We are looking for a student who:

• IscurrentlypursuinganBachelororMasterinMechanical Engineering Applied MathematicsComputer ScienceDataScienceor a related technicalfield.

• Hasexperience with Python and ideallydeep learning frameworkssuch asPyTorchor TensorFlow.

• Hasfamiliarity with 3Dgeometryformats orCFDsimulation and numericaldata.

• Hasa strong interest inphysics-based modelingand applying AI to engineering problems.

• Communicates fluently inEnglish.

What we offer

• Mentoring atacademiclevelwill be available throughout the internship.

• Internship/graduation fee and travel allowancewill be paid for the duration of the assignment.

• Opportunity to contribute to ahigh-impact innovation projectin collaboration with leading maritimecompaniesinstitutesand universities.

• Research publication islikelypossiblewitha possible extensionof the internship period.

• Possibility to visitpartner hubs or research centers(e.g. MARIN in Wageningen) depending on project needs and availability